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An artificial agent for financial risk and returns' prediction is built witha modular cognitive system comprised of interconnected recurrent neuralnetworks, such that the agent learns to predict the financial returns, andlearns to predict the squared deviation around these predicted returns. Thesetwo expectations are used to build a volatility-sensitive interval predictionfor financial returns, which is evaluated on three major financial indices andshown to be able to predict financial returns with higher than 80% success ratein interval prediction in both training and testing, raising into question theEfficient Market Hypothesis. The agent is introduced as an example of a classof artificial intelligent systems that are equipped with a Modular NetworkedLearning cognitive system, defined as an integrated networked system of machinelearning modules, where each module constitutes a functional unit that istrained for a given specific task that solves a subproblem of a complex mainproblem expressed as a network of linked subproblems. In the case of neuralnetworks, these systems function as a form of an "artificial brain", where eachmodule is like a specialized brain region comprised of a neural network with aspecific architecture.

Financial Risk and Returns Prediction with Modular Networked Learning